Customer Lifetime Value Formula: How to Calculate CLV
The CLV/LTV formula explained in plain terms: the simple revenue model, the gross-margin adjustment, and the churn-based shortcut for subscription businesses, each with a worked example.
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What customer lifetime value means
Customer lifetime value (CLV) is a planning estimate of how much revenue — or, more usefully, how much gross profit — a business expects to earn from an average customer relationship, from the point a customer starts buying until they're expected to stop. "Lifetime value" and "LTV" refer to the same idea and are used interchangeably in most business writing on the topic, including this guide. A handful of organizations draw a more specific internal distinction between the two terms, but there's no single agreed-upon convention that every reputable source applies the same way, so treat CLV and LTV as synonyms unless a specific source tells you otherwise.
"Lifetime" doesn't mean a literal, known lifespan you could look up for any one customer — it means the expected length of an economic relationship, estimated from patterns like purchase frequency or churn rate. CLV describes an average, forward-looking expectation across a group of customers, not a fact about any specific one.
More than one formula is used to estimate CLV, and which one fits depends on your business model. This guide walks through the three most common: a simple revenue-based model, a gross-margin-adjusted version of it, and a churn-based shortcut for subscription businesses. To run any of them with your own numbers, the Customer Lifetime Value (CLV/LTV) Calculator does the arithmetic for you in both modes.
How the pieces fit together
That's the starting point for the simple model below. The subscription/churn model further down reaches the same kind of result from different inputs — average revenue per period and a churn rate — because subscription businesses don't really have a separate "purchase frequency" to measure.
The simple revenue model
Start here if your customers make discrete, separate purchases — e-commerce, a repeat-service business, anything where you can count "orders." It needs three inputs, and all three should describe the same customer population over compatible time periods.
- Average value per purchase. Total revenue over a period divided by the number of purchases in that period — what a typical order is worth.
- Purchase frequency. How many purchases a typical customer makes per year. Customers who buy every couple of months might land around 4–6; customers who buy once every year or two will be well below 1.
- Average customer lifespan. How many years a typical customer keeps buying from you before they stop.
Revenue CLV = Average value per purchase × Purchase frequency × Average customer lifespan
Keep the same population in view across all three inputs — average order value and purchase frequency should describe the same group of customers you're using to estimate lifespan, not, for example, order value from your entire customer base paired with a lifespan estimate from only your longest-tenured customers.
Worked example: a repeat-purchase business
A specialty home-goods retailer looks at its repeat customers and finds:
- Average value per purchase = $300
- Purchase frequency = 2 purchases per year
- Average customer lifespan = 5 years
Annual revenue per customer = $300 × 2 = $600
Revenue CLV = $600 × 5 = $3,000
On paper, a typical customer is worth $3,000 in revenue over five years. That's a useful first estimate, but it's revenue, not profit — the next section adjusts this same example to show why that distinction matters.
Why revenue alone can overstate value: gross-profit CLV
Revenue CLV counts every dollar a customer spends, but the business doesn't keep all of it — some of it pays for the direct cost of the product or service itself (materials, production, delivery, fulfillment, and similar costs). Gross margin is the share of revenue left after those direct costs, expressed as a percentage. Applying it to revenue CLV gives a more decision-useful figure: what the business actually keeps before overhead, marketing, and taxes.
Gross-profit CLV = Revenue CLV × Gross margin ÷ 100
(If your gross margin is already written as a decimal — 0.40 rather than 40% — drop the ÷ 100 and multiply directly.)
Back to the retailer above: at a 40% gross margin, gross-profit CLV = $3,000 × 40% = $1,200. That's the figure worth comparing against acquisition cost, not the $3,000 revenue figure — a customer who generates a lot of revenue but costs a lot to serve can end up worth less than a lower-revenue customer with a leaner cost structure.
The churn-based shortcut for subscription businesses
Subscription businesses don't really have "purchase frequency" and "lifespan" as separate things to measure — a customer either keeps paying each period or cancels. For these businesses, CLV is usually estimated from average revenue per customer and a churn rate instead.
The starting idea: if a roughly fixed share of customers cancel every period, the expected number of periods a typical customer sticks around is approximately the reciprocal of the churn rate.
Expected customer lifespan ≈ 1 ÷ Churn rate
Revenue CLV = Average revenue per customer ÷ Churn rate
Gross-profit CLV = (Average revenue per customer × Gross margin)
÷ Churn rate
Every input here has to describe the same period — a monthly churn rate needs a monthly revenue figure, not an annual one (more on this below). Churn rate also needs to be greater than 0%; at exactly 0% churn the reciprocal is undefined, which is really just the model telling you it can't estimate a lifespan for customers who never leave.
This shortcut is an approximation, not an exact law, and it rests on a few assumptions worth knowing:
- It assumes churn behaves like a roughly constant, period-over-period probability — the same "chance of leaving" every period — rather than, say, a wave of early cancellations that tapers off later. Real retention curves can be steeper early on and flatter later, a pattern this shortcut doesn't capture.
- It assumes the customers you're measuring churn on are a reasonably stable, representative group — not a cohort still in an unusually volatile early period, and not several very different acquisition cohorts blended into one churn number.
- It's a reasonable planning approximation for many subscription businesses, but it isn't a substitute for a proper cohort retention curve when the stakes are high enough to justify building one.
Worked example: a subscription business
A subscription service reviews one customer segment and finds:
- Average revenue per customer = $80 per month
- Monthly churn rate = 2.5%
- Gross margin = 70%
Expected lifespan ≈ 1 ÷ 2.5% = 40 months (≈ 3.33 years)
Revenue CLV = $80 ÷ 2.5% = $3,200
Gross-profit CLV = ($80 × 70%) ÷ 2.5% = $56 ÷ 2.5% = $2,240
A typical customer in this segment is expected to generate $3,200 in revenue and $2,240 in gross profit before they cancel — over an expected 40 months, not a guaranteed 40 months. Some customers will churn in month two; others will stay for years. The $2,240 figure is a planning average across the segment, not a promise about any one customer.
Why the time period has to match
Every formula above breaks the same way if its inputs describe different time periods: multiplying a monthly revenue figure by an annual churn rate, or pairing an annual purchase frequency with a lifespan measured in months, produces a number that looks precise but doesn't correspond to anything real. None of these formulas can detect that kind of mismatch on their own — a wrong-period input still produces an answer, just a meaningless one.
| Figure | Paired with | Compatible? |
|---|---|---|
| Monthly revenue per customer | Monthly churn rate | Yes |
| Monthly revenue per customer | Annual churn rate | No — convert one first |
| Annual revenue per customer | Annual churn rate | Yes |
| Purchase frequency (per year) | Lifespan (in years) | Yes — already aligned |
If your revenue and churn figures come in different periods, converting one to match the other takes care: revenue divides roughly evenly by 12 to go from annual to monthly, but churn doesn't — an annual churn rate is not simply a monthly rate times 12, because cancellations compound over the year. The safer path is usually to gather revenue and churn for the same period in the first place, rather than converting between them after the fact.
How CLV connects to customer acquisition cost
CLV only tells half of the story — it says nothing about what it cost to win the customer in the first place. Pairing it with Customer Acquisition Cost shows whether the value a customer brings is worth what it took to acquire them, typically expressed as an LTV:CAC ratio. Run both sides together on the LTV:CAC Ratio Calculator, and see the full mechanics — gross-profit vs revenue basis, cohort consistency, common mistakes — in the LTV:CAC Ratio Explained guide. This guide stays focused on CLV itself.
Common mistakes
- Mixing monthly and annual periods. A monthly revenue figure paired with an annual churn rate (or vice versa) produces a number that looks precise but isn't real — see the table above.
- Treating revenue CLV as if it were profit. Revenue CLV hasn't been reduced by the cost of serving the customer; gross-profit CLV is the more decision-useful figure for most planning purposes.
- Using churn from a different cohort or period than the revenue figure. Churn measured on one acquisition cohort paired with average revenue from your whole customer base blends two different populations into one number.
- Assuming every customer behaves like the average. A single blended CLV can hide large differences between segments — see below.
- Estimating a multi-year lifespan from a few months of data. A churn rate measured in a business's first few months can be unusually high or unusually low compared to its long-run rate, and a short history may not capture how retention settles down.
- Leaving out refunds, discounts, or servicing costs that belong in gross margin. If your gross margin figure doesn't already reflect these, CLV calculated from it will overstate what the business actually keeps.
One average can hide big differences: segmenting CLV
A single company-wide CLV figure blends together customers who may behave very differently. In some businesses, for example, a customer acquired through a referral sticks around longer than one acquired through a discount-driven ad campaign, or an enterprise-tier customer has both a higher revenue figure and a different churn pattern than a self-serve customer on the cheapest plan. Common ways to segment CLV include acquisition channel, plan or pricing tier, geography, how long a customer has already stuck around, and product line. You don't need every segment split out to start — but if one segment is unusually large or unusually different from the rest, a single blended average can hide that it's dragging the overall number up or down.
Limitations
- CLV is a planning estimate built on stated assumptions about future revenue, margin, and retention — not a guarantee of what any individual customer will actually be worth.
- None of the models above discount future cash flows to present value; a dollar of CLV expected in year four is treated the same as a dollar today.
- These models assume revenue, margin, and churn stay roughly stable over the customer's lifetime, which is rarely exactly true — see "Common mistakes" above.
- This is a planning model, not accounting, valuation, investment, or tax advice.
How this guide differs from the CLV calculator
This guide explains which formula to use and why. The Customer Lifetime Value (CLV/LTV) Calculator runs the arithmetic instantly for your own numbers, in both the subscription/churn and transactional/purchase-behavior modes described above, including an optional portfolio-wide estimate across your active customers. Use this guide to decide which model fits your business and to see how the pieces fit together; use the calculator once you're ready to plug in your own average revenue, margin, churn, or purchase figures.
Sources
- Shopify — "What Is Customer Lifetime Value? How to Calculate CLV". Cited for the average-value-per-purchase × purchase-frequency × lifespan formula and its component definitions, and for CLV and LTV being used interchangeably.
- HubSpot — "How to calculate customer lifetime value (CLV) & why it matters". Cited for the customer-value component definitions and for the churn-based lifespan shortcut.
- Stripe — "What Is Customer Lifetime Value (CLV)?". Cited for the gross-margin/profit-basis distinction between CLV models and for how thin margins can make a high-revenue customer worth less than a higher-margin one.
See the Methodology page for how sources are selected across this site.